nodes_model_advanced.py 10.5 KB
Newer Older
1
2
3
import folder_paths
import comfy.sd
import comfy.model_sampling
4
import comfy.latent_formats
comfyanonymous's avatar
comfyanonymous committed
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
import torch

class LCM(comfy.model_sampling.EPS):
    def calculate_denoised(self, sigma, model_output, model_input):
        timestep = self.timestep(sigma).view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
        x0 = model_input - model_output * sigma

        sigma_data = 0.5
        scaled_timestep = timestep * 10.0 #timestep_scaling

        c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2)
        c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5

        return c_out * x0 + c_skip * model_input

21
22
23
24
class X0(comfy.model_sampling.EPS):
    def calculate_denoised(self, sigma, model_output, model_input):
        return model_output

25
class ModelSamplingDiscreteDistilled(comfy.model_sampling.ModelSamplingDiscrete):
26
27
    original_timesteps = 50

28
29
    def __init__(self, model_config=None):
        super().__init__(model_config)
comfyanonymous's avatar
comfyanonymous committed
30

31
        self.skip_steps = self.num_timesteps // self.original_timesteps
comfyanonymous's avatar
comfyanonymous committed
32

33
        sigmas_valid = torch.zeros((self.original_timesteps), dtype=torch.float32)
34
        for x in range(self.original_timesteps):
35
            sigmas_valid[self.original_timesteps - 1 - x] = self.sigmas[self.num_timesteps - 1 - x * self.skip_steps]
comfyanonymous's avatar
comfyanonymous committed
36

37
        self.set_sigmas(sigmas_valid)
comfyanonymous's avatar
comfyanonymous committed
38
39
40
41

    def timestep(self, sigma):
        log_sigma = sigma.log()
        dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
42
        return (dists.abs().argmin(dim=0).view(sigma.shape) * self.skip_steps + (self.skip_steps - 1)).to(sigma.device)
comfyanonymous's avatar
comfyanonymous committed
43
44

    def sigma(self, timestep):
45
        t = torch.clamp(((timestep.float().to(self.log_sigmas.device) - (self.skip_steps - 1)) / self.skip_steps).float(), min=0, max=(len(self.sigmas) - 1))
comfyanonymous's avatar
comfyanonymous committed
46
47
48
49
        low_idx = t.floor().long()
        high_idx = t.ceil().long()
        w = t.frac()
        log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
50
        return log_sigma.exp().to(timestep.device)
comfyanonymous's avatar
comfyanonymous committed
51

52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75

def rescale_zero_terminal_snr_sigmas(sigmas):
    alphas_cumprod = 1 / ((sigmas * sigmas) + 1)
    alphas_bar_sqrt = alphas_cumprod.sqrt()

    # Store old values.
    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()

    # Shift so the last timestep is zero.
    alphas_bar_sqrt -= (alphas_bar_sqrt_T)

    # Scale so the first timestep is back to the old value.
    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)

    # Convert alphas_bar_sqrt to betas
    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
    alphas_bar[-1] = 4.8973451890853435e-08
    return ((1 - alphas_bar) / alphas_bar) ** 0.5

class ModelSamplingDiscrete:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "model": ("MODEL",),
76
                              "sampling": (["eps", "v_prediction", "lcm", "x0"],),
77
78
79
80
81
82
83
84
85
86
87
                              "zsnr": ("BOOLEAN", {"default": False}),
                              }}

    RETURN_TYPES = ("MODEL",)
    FUNCTION = "patch"

    CATEGORY = "advanced/model"

    def patch(self, model, sampling, zsnr):
        m = model.clone()

comfyanonymous's avatar
comfyanonymous committed
88
        sampling_base = comfy.model_sampling.ModelSamplingDiscrete
89
90
91
92
        if sampling == "eps":
            sampling_type = comfy.model_sampling.EPS
        elif sampling == "v_prediction":
            sampling_type = comfy.model_sampling.V_PREDICTION
comfyanonymous's avatar
comfyanonymous committed
93
94
        elif sampling == "lcm":
            sampling_type = LCM
95
            sampling_base = ModelSamplingDiscreteDistilled
96
97
        elif sampling == "x0":
            sampling_type = X0
98

comfyanonymous's avatar
comfyanonymous committed
99
        class ModelSamplingAdvanced(sampling_base, sampling_type):
100
101
            pass

comfyanonymous's avatar
comfyanonymous committed
102
        model_sampling = ModelSamplingAdvanced(model.model.model_config)
103
104
        if zsnr:
            model_sampling.set_sigmas(rescale_zero_terminal_snr_sigmas(model_sampling.sigmas))
comfyanonymous's avatar
comfyanonymous committed
105

106
107
108
        m.add_object_patch("model_sampling", model_sampling)
        return (m, )

109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
class ModelSamplingStableCascade:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "model": ("MODEL",),
                              "shift": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step":0.01}),
                              }}

    RETURN_TYPES = ("MODEL",)
    FUNCTION = "patch"

    CATEGORY = "advanced/model"

    def patch(self, model, shift):
        m = model.clone()

        sampling_base = comfy.model_sampling.StableCascadeSampling
        sampling_type = comfy.model_sampling.EPS

        class ModelSamplingAdvanced(sampling_base, sampling_type):
            pass

        model_sampling = ModelSamplingAdvanced(model.model.model_config)
        model_sampling.set_parameters(shift)
        m.add_object_patch("model_sampling", model_sampling)
        return (m, )

comfyanonymous's avatar
comfyanonymous committed
135
136
137
138
class ModelSamplingSD3:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "model": ("MODEL",),
139
                              "shift": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step":0.01}),
comfyanonymous's avatar
comfyanonymous committed
140
141
142
143
144
145
146
                              }}

    RETURN_TYPES = ("MODEL",)
    FUNCTION = "patch"

    CATEGORY = "advanced/model"

147
    def patch(self, model, shift, multiplier=1000):
comfyanonymous's avatar
comfyanonymous committed
148
149
150
151
152
153
154
155
156
        m = model.clone()

        sampling_base = comfy.model_sampling.ModelSamplingDiscreteFlow
        sampling_type = comfy.model_sampling.CONST

        class ModelSamplingAdvanced(sampling_base, sampling_type):
            pass

        model_sampling = ModelSamplingAdvanced(model.model.model_config)
157
        model_sampling.set_parameters(shift=shift, multiplier=multiplier)
comfyanonymous's avatar
comfyanonymous committed
158
159
160
        m.add_object_patch("model_sampling", model_sampling)
        return (m, )

161
162
163
164
165
166
167
168
169
170
171
172
class ModelSamplingAuraFlow(ModelSamplingSD3):
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "model": ("MODEL",),
                              "shift": ("FLOAT", {"default": 1.73, "min": 0.0, "max": 100.0, "step":0.01}),
                              }}

    FUNCTION = "patch_aura"

    def patch_aura(self, model, shift):
        return self.patch(model, shift, multiplier=1.0)

comfyanonymous's avatar
comfyanonymous committed
173
174
175
176
class ModelSamplingContinuousEDM:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "model": ("MODEL",),
177
                              "sampling": (["v_prediction", "edm_playground_v2.5", "eps"],),
comfyanonymous's avatar
comfyanonymous committed
178
179
180
181
182
183
184
185
186
187
188
189
                              "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
                              "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
                              }}

    RETURN_TYPES = ("MODEL",)
    FUNCTION = "patch"

    CATEGORY = "advanced/model"

    def patch(self, model, sampling, sigma_max, sigma_min):
        m = model.clone()

190
191
        latent_format = None
        sigma_data = 1.0
comfyanonymous's avatar
comfyanonymous committed
192
193
194
195
        if sampling == "eps":
            sampling_type = comfy.model_sampling.EPS
        elif sampling == "v_prediction":
            sampling_type = comfy.model_sampling.V_PREDICTION
196
197
198
199
        elif sampling == "edm_playground_v2.5":
            sampling_type = comfy.model_sampling.EDM
            sigma_data = 0.5
            latent_format = comfy.latent_formats.SDXL_Playground_2_5()
comfyanonymous's avatar
comfyanonymous committed
200
201
202
203

        class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousEDM, sampling_type):
            pass

204
        model_sampling = ModelSamplingAdvanced(model.model.model_config)
205
        model_sampling.set_parameters(sigma_min, sigma_max, sigma_data)
comfyanonymous's avatar
comfyanonymous committed
206
        m.add_object_patch("model_sampling", model_sampling)
207
208
        if latent_format is not None:
            m.add_object_patch("latent_format", latent_format)
comfyanonymous's avatar
comfyanonymous committed
209
210
        return (m, )

211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
class ModelSamplingContinuousV:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "model": ("MODEL",),
                              "sampling": (["v_prediction"],),
                              "sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
                              "sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
                              }}

    RETURN_TYPES = ("MODEL",)
    FUNCTION = "patch"

    CATEGORY = "advanced/model"

    def patch(self, model, sampling, sigma_max, sigma_min):
        m = model.clone()

        latent_format = None
        sigma_data = 1.0
        if sampling == "v_prediction":
            sampling_type = comfy.model_sampling.V_PREDICTION

        class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousV, sampling_type):
            pass

        model_sampling = ModelSamplingAdvanced(model.model.model_config)
        model_sampling.set_parameters(sigma_min, sigma_max, sigma_data)
        m.add_object_patch("model_sampling", model_sampling)
        return (m, )

comfyanonymous's avatar
comfyanonymous committed
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
class RescaleCFG:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "model": ("MODEL",),
                              "multiplier": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
                              }}
    RETURN_TYPES = ("MODEL",)
    FUNCTION = "patch"

    CATEGORY = "advanced/model"

    def patch(self, model, multiplier):
        def rescale_cfg(args):
            cond = args["cond"]
            uncond = args["uncond"]
            cond_scale = args["cond_scale"]
            sigma = args["sigma"]
258
            sigma = sigma.view(sigma.shape[:1] + (1,) * (cond.ndim - 1))
comfyanonymous's avatar
comfyanonymous committed
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
            x_orig = args["input"]

            #rescale cfg has to be done on v-pred model output
            x = x_orig / (sigma * sigma + 1.0)
            cond = ((x - (x_orig - cond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma)
            uncond = ((x - (x_orig - uncond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma)

            #rescalecfg
            x_cfg = uncond + cond_scale * (cond - uncond)
            ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True)
            ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True)

            x_rescaled = x_cfg * (ro_pos / ro_cfg)
            x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg

            return x_orig - (x - x_final * sigma / (sigma * sigma + 1.0) ** 0.5)

        m = model.clone()
        m.set_model_sampler_cfg_function(rescale_cfg)
        return (m, )

280
281
NODE_CLASS_MAPPINGS = {
    "ModelSamplingDiscrete": ModelSamplingDiscrete,
comfyanonymous's avatar
comfyanonymous committed
282
    "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM,
283
    "ModelSamplingContinuousV": ModelSamplingContinuousV,
284
    "ModelSamplingStableCascade": ModelSamplingStableCascade,
comfyanonymous's avatar
comfyanonymous committed
285
    "ModelSamplingSD3": ModelSamplingSD3,
286
    "ModelSamplingAuraFlow": ModelSamplingAuraFlow,
comfyanonymous's avatar
comfyanonymous committed
287
    "RescaleCFG": RescaleCFG,
288
}